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Inferring causality [PDF]

Journal of Vascular Surgery, 2020
Daniel J, Wong, Elliot L, Chaikof
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Nonparametric inference for interventional effects with multiple mediators

Journal of Causal Inference, 2021
Understanding the pathways whereby an intervention has an effect on an outcome is a common scientific goal. A rich body of literature provides various decompositions of the total intervention effect into pathway-specific effects.
Benkeser David, Ran Jialu
doaj   +1 more source

Incremental intervention effects in studies with dropout and many timepoints#

Journal of Causal Inference, 2021
Modern longitudinal studies collect feature data at many timepoints, often of the same order of sample size. Such studies are typically affected by dropout and positivity violations.
Kim Kwangho, Kennedy Edward H., Naimi Ashley I.   +2 more
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Revisiting a Discrepant Result: A Propensity Score Analysis, the Paired Availability Design for Historical Controls, and a Meta-Analysis of Randomized Trials

Journal of Causal Inference, 2013
There is an ongoing controversy over whether epidural analgesia for women in labor increases the probability of Caesarean section. Previous research compared results from three methods for estimating the effect of epidural analgesia on the probability of
G. Baker Stuart, S. Lindeman Karen
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Randomization Inference in the Regression Discontinuity Design: An Application to Party Advantages in the U.S. Senate

Journal of Causal Inference, 2015
In the Regression Discontinuity (RD) design, units are assigned a treatment based on whether their value of an observed covariate is above or below a fixed cutoff.
Cattaneo Matias D., Frandsen Brigham R., Titiunik Rocío   +2 more
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Behavioural Causal Inference

Review of Economic Studies
Abstract When inferring causal effects from correlational data, a common practice by professional researchers but also lay people is to control for potential confounders. Inappropriate controls produce erroneous causal inferences. I model decision-makers (DMs) who use endogenous observational data to learn actions’ causal effect on ...
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Targeting mediating mechanisms of social disparities with an interventional effects framework, applied to the gender pay gap in Western Germany

Journal of Causal Inference
The Oaxaca-Blinder (OB) decomposition is a widely used method to explain social disparities. However, assigning causal meaning to its estimated components requires strong assumptions that often lack explicit justification.
Didden Christiane
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To Adjust or Not to Adjust? Sensitivity Analysis of M-Bias and Butterfly-Bias

Journal of Causal Inference, 2015
“M-Bias,” as it is called in the epidemiologic literature, is the bias introduced by conditioning on a pretreatment covariate due to a particular “M-Structure” between two latent factors, an observed treatment, an outcome, and a “collider.” This ...
Ding Peng, Miratrix Luke W.
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Role of placebo samples in observational studies

Journal of Causal Inference
In an observational study, it is common to leverage known null effects to detect bias. One such strategy is to set aside a placebo sample – a subset of data immune from the hypothesized cause-and-effect relationship. Existence of an effect in the placebo
Ye Ting, He Qijia, Chen Shuxiao, Zhang Bo   +3 more
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Design and Analysis of Experiments in Networks: Reducing Bias from Interference

Journal of Causal Inference, 2016
Estimating the effects of interventions in networks is complicated due to interference, such that the outcomes for one experimental unit may depend on the treatment assignments of other units.
Eckles Dean, Karrer Brian, Ugander Johan
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